基于WebGIS的森林旅游信息管理网站的构建

文章分析了旅游网站的现状、森林旅游的特点,介绍了基于WebGIS技术开发森林旅游信息管理网站的优势和系统设计方法。通过森林旅游网站的构建实现面向公众的森林旅游信息交流和面向管理部门的信息管理功能。     

基于物联网的远程温室视觉监控系统设计与实现

在当前智慧农业的大环境下,农作物生长过程的识别与监控问题一直是一项具有挑战性的任务,基于此提出一种基于物联网的远程温室视觉监控系统,系统通过LoRa无线通信技术监测温室内的温湿度、光照强度等环境参数,能够及时监测到农作物的生长状况,并实现自动通风、自动补光等功能。在PC端的Qt上位机实时监测温室内的环境信息并控制环境参数,通过OV9726摄像头对农作物进行监测,所获得的生长状态信息传输到S3C6410集中控制模块进行处理,结合克隆选择算法和朴素贝叶斯分类器对叶片进行识别处理。本系统采用LoRa模块进行自组网来实现环境监测,将Linux操作系统移植到集中控制模块,为视觉系统软硬件平台的搭建做准备工作,所使用的组合算法能够使得农作物叶片识别率达到95.3%,识别时间达到8.4 ms,对于叶片识别精度等方面有着明显的提升,经过实验充分验证本系统所使用的设备与算法的有效性。     

基于物联网的日光温室环境监测在线校准

[目的]温室环境条件特别是温度对于作物生长和发育具有十分显著的影响。日光温室调控的主要环境因子之一是温度。然而,自然环境下的光照对温度产生作用,影响空气温度的监测精度。大多温度传感要求将传感器置于避光处,然而实际应用中难以保证。[方法]采用机器学习中的支持向量机算法(SVM),对日光温室内的温度智能监测算法进行了研究,根据光照情况对实时监测的温度数据进行校准。[结果]通过与实验测量的数据进行对比分析,结果表明,所提出的监测方法可以较为准确地实时监测空气温度,从而无需使用隔热材料或者遮阳处理,就可以基于监测的数据更精确地对相应的环境因素进行调节。[结论]基于该方法,可采用常用的工业设备实现温室大棚内实时温度数据的监测,既可以节约设备和人力成本,又可以为温室控制提供准确的数据。     

浙江龙泉地区珍贵阔叶树种保护培育工作现状及对策

为了促进人与自然和谐相处,保障生态文明建设,应做好森林资源保护培育工作。通过分析浙江龙泉地区珍贵阔叶树种保护培育工作的现状及问题,提出相应的对策,以完善龙泉地区森林资源建设,促进林业可持续发展。     

Deep neural network algorithm for estimating maize biomass based on simulated Sentinel 2A vegetation indices and leaf area index

Accurate estimation of biomass is necessary for evaluating crop growth and predicting crop yield.Biomass is also a key trait in increasing grain yield by crop breeding.The aims of this study were(i)to identify the best vegetation indices for estimating maize biomass,(ii)to investigate the relationship between biomass and leaf area index(LAI)at several growth stages,and(iii)to evaluate a biomass model using measured vegetation indices or simulated vegetation indices of Sentinel 2A and LAI using a deep neural network(DNN)algorithm.The results showed that biomass was associated with all vegetation indices.The three-band water index(TBWI)was the best vegetation index for estimating biomass and the corresponding R2,RMSE,and RRMSE were 0.76,2.84 t ha−1,and 38.22%respectively.LAI was highly correlated with biomass(R2=0.89,RMSE=2.27 t ha−1,and RRMSE=30.55%).Estimated biomass based on 15 hyperspectral vegetation indices was in a high agreement with measured biomass using the DNN algorithm(R2=0.83,RMSE=1.96 t ha−1,and RRMSE=26.43%).Biomass estimation accuracy was further increased when LAI was combined with the 15 vegetation indices(R2=0.91,RMSE=1.49 t ha−1,and RRMSE=20.05%).Relationships between the hyperspectral vegetation indices and biomass differed from relationships between simulated Sentinel 2A vegetation indices and biomass.Biomass estimation from the hyperspectral vegetation indices was more accurate than that from the simulated Sentinel 2A vegetation indices(R2=0.87,RMSE=1.84 t ha−1,and RRMSE=24.76%).The DNN algorithm was effective in improving the estimation accuracy of biomass.It provides a guideline for estimating biomass of maize using remote sensing technology and the DNN algorithm in this region.     

Exploring tree diversity and stand structure of savanna woodlands in southeastern Sudan

Savanna woodlands in Sudan host great biodiversity, provide a plethora of ecosystem goods and services to local communities, and sustain numerous ecological functions. Although the importance of the Acacia trees in these areas is well known, up-to-date information about these woodlands' diversity is limited and changes in their woody vegetation composition, density, diversity and relative frequency are not monitored over time. This study explored tree diversity and stand stage structure in Nuara Reserved Forest, a typical savanna woodland ecosystem in southeastern Sudan. A total of 638 circular sample plots (1000 m² for each) were established using a systematic sampling grid method. The distance between plots was 200 m. In each plot, all living trees with diameter at breast height (DBH) ≥5.00 cm were identified and counted, and their DBH values were recorded. From these data, tree composition, diversity, density and stage structure were assessed. There were 12,259 individual trees representing four species (Acacia seyal, Balanites aegyptiaca, Acacia Senegal and Acacia mellifera) that belong to two families. The dominant species was Acacia seyal. Average tree density was 191 trees/hm² and the Shannon-Weiner index for trees diversity was 0.204. Overall, young trees comprised 86.30% of the forest. The state of tree richness and density in the study area was low compared to other similar environments in the region and around the world. We recommended adoption of a proper management system that includes monitoring of woody vegetation diversity in this forest, and management actions to enhance tree diversity and sustain ecosystem services to local communities. In addition to care for the dominant Acacia seyal stands, more attention and conservation should be devoted to reestablishing Acacia senegal and Acacia mellifera trees because of their high ecological and economic values for local communities.     

重庆市不同规模灌区农灌水有效利用系数测算与对比研究

通过对重庆市不同规模灌区农田灌溉用水有效利用系数计算结果的比较研究,对出现的中型灌区农田灌溉用水有效利用系数大于小型灌区的结果进行合理性分析,并为重庆市农田灌溉用水有效利用系数的进一步提高,提出相关建议,为政府相关部门的科学决策提供参考.综合考虑重庆的地形、气候、土壤等因素.根据选定灌区的实测数据,采用首尾测算法计算2017年重庆市不同规模灌溉农田灌溉水的有效利用系数,采用单因素和主成分分析法,分析影响农田灌溉水有效灌溉系数的主要因素. 1)通过测算, 2017年重庆市中型灌区农田灌溉水有效利用系数为0.499,小型灌区为0.487,推算全市为0.495; 2)在影响农田灌溉水有效利用系数的主要因素中,当地的节水工程和管理状况所占的比例大于自然因素; 3)中型灌区在节水工程的投资、管理和维护等方面均优于小型灌区; 4)自然因素中,降水量对农田灌溉水有效利用系数具有一定的负面影响.     

凤阳山不同林分土壤腐殖质特征及影响因素

以浙江省凤阳山海拔1 300~1 400 m处不同林分类型(阔叶混交林、针阔混交林、杉木林、竹林)为对象,测定不同土层土壤基本理化性质、酶活性及腐殖质质量分数,分析土壤腐殖质特征及影响因素,为凤阳山自然保护区的土壤肥力和可持续经营发展提供理论依据。结果表明:土壤腐殖质质量分数针阔混交林最高,阔叶混交林、竹林、杉木林次之;胡敏酸质量分数针阔混交林最高,杉木林最低;富里酸质量分数针阔混交林最高,竹林最低。除磷酸酶活性随土层加深无统一规律外,4种林分脲酶活性、过氧化氢酶活性及蔗糖酶活性土壤随土层加深皆呈现降低趋势。土壤pH值、土壤密度与土壤腐殖质质量分数、胡敏酸质量分数和富里酸质量分数均呈现显著的负相关;除磷酸酶活性相关性不显著外,土壤脲酶活性、蔗糖酶活性、过氧化氢酶活性、总孔隙度、毛管孔隙、非毛管孔隙与土壤腐殖质质量分数、胡敏酸质量分数和富里酸质量分数均呈现显著的正相关。凤阳山不同林分类型对土壤腐殖质特征的影响较土层深度更显著,土壤理化性质及酶活性与土壤腐殖质质量分数有着密切关系。     

运用植被指数时序特征对落叶松人工林分类

为探究植被指数时序特征是否有利于落叶松人工林提取,以孟家岗林场为研究试验区域,根据落叶松人工林季相和物候特征,利用Landsat8OLI影像数据提取研究区内5种植被的归一化植被指数(I NDV)、差值植被指数(I DV)、比值植被指数(I RV)、增强型植被指数(I EV),构建相应的植被指数时序特征。采用最大似然和随机森林两种方法对单一时相影像和加入植被指数时序特征的影像进行对比试验。结果表明:影像中加入植被指数时序特征后,最大似然算法的分类总体精度为89.53%,Kappa系数为0.87,比单一时序特征的影像分类精度提高了13.35%;随机森林算法的森林类型分类总体精度为93.22%,Kappa系数为0.92,比单一时序特征的影像分类精度提高了19.8%。因此,加入植被指数时序特征后能得到更高的落叶松人工林提取精度。     

基于无人机高光谱遥感的柑橘患病植株分类与特征波段提取

目的 结合传统与现代农业病虫害监测的优缺点,探索通过无人机高光谱遥感技术检测出患病的柑橘植株、通过人工田间调查方式判断其患病种类及患病程度的病虫害监测方法。方法 使用无人机获取原始高光谱图像,经过光谱预处理和特征工程后,采用连续投影算法提取对柑橘患病植株分类贡献值最大的特征波长组合,基于全波段使用BP神经网络和XgBoost算法、基于特征波段使用逻辑回归和支持向量机算法,建立分类模型。结果 基于全波段的BP神经网络和XgBoost算法的ROC曲线下面积(Area under curve,AUC)分别为0.883 0和0.912 0,分类准确率均超过95%;提取出698和762 nm的特征波长组合,基于特征波长使用逻辑回归和支持向量机算法建立的分类模型召回率分别达到了93.00%和96.00%。结论 基于特征波长建模在患病样本分类中表现出很高的准确率,证明了特征波长组合的有效性。本研究结果可为柑橘种植园的病虫害监测提供一定的数据和理论支撑。